isprsarchives XL 1 W3 139 2013

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W3, 2013
SMPR 2013, 5 – 8 October 2013, Tehran, Iran

A New Developed GIHS-BT-SFIM Fusion Method Based On Edge and Class Data
S. Dehnavi1, A. Mohammadzadeh1
1

Faculty of Geodesy and Geomatics, K.N.Toosi University of Technology, Tehran, Iran.
dehnavi.kntu@gmail.com
almoh2@gmail.com

KEY WORDS: GIHS_BT_SFIM, Fusion, Edge adaptive, Class based

ABSTRACT
The objective of image fusion (or sometimes pan sharpening) is to produce a single image containing the best aspects of the
source images. Some desirable aspects are high spatial resolution and high spectral resolution. With the development of space
borne imaging sensors, a unified image fusion approach suitable for all employed imaging sources becomes necessary. Among
various image fusion methods, intensity-hue-saturation (IHS) and Brovey Transforms (BT) can quickly merge huge amounts of
imagery. However they often face color distortion problems with fused images. The SFIM fusion is one of the most frequently
employed approaches in practice to control the tradeoff between the spatial and spectral information. In addition it preserves
more spectral information but suffer more spatial information loss. Its effectiveness is heavily depends on the filter design. In this

work, two modifications were tested to improve the spectral quality of the images and also investigating class-based fusion
results. First, a Generalized Intensity-Hue-Saturation (GIHS), Brovey Transform (BT) and smoothing-filter based intensity
modulation (SFIM) approach was implemented. This kind of algorithm has shown computational advantages among other fusion
methods like wavelet, and can be extended to different number of bands as in literature discussed. The used IHS-BT-SFIM
algorithm incorporates IHS, IHS-BT, BT, BT-SFIM and SFIM methods by two adjustable parameters. Second, a method was
proposed to plus edge information in previous GIHS_BT_SFIM and edge enhancement by panchromatic image. Adding
panchromatic data to images had no much improvement. Third, an edge adaptive GIHS_BT_SFIM was proposed to enforce
fidelity away from the edges. Using MS image off edges has shown spectral improvement in some fusion methods. Fourth, a
class based fusion was tested, which tests different coefficients for each method due to its class. The best parameters for
vegetated areas was k1=0.6, k2=0.8; and for urban region it was k1=0.4, k2=0.4. Results might be useful for future studies on
fusion methods and their generalization.

1. INTRODUCTION

I

N RECENT years, the number of spectral bands in a
satellite
image
has

increased
significantly.
Additionally, the spatial resolution of the image has
also been enhanced. For remote sensing applications, the
satellite images which can simultaneously offer high spatial
and spectral resolutions are often required. It means that the
ground and spectrum details must be accurately presented
by a single image (Tu, 2012). The benefit of merged image
has been demonstrated in many practical applications,
especially for vegetation, land-use, precision farming, and
urban studies (Tu, 2001). Due to this, various methods for
image fusion have been described earlier (Amolins, 2007;
Tu, 2001, 2004, 2005, 2012; Duo, 2007; Sh. Li, 2002; Z.
Li,2005; Ling, 2007; Pajares, 2004; Yang, 2007; Rahmani,
2010; Chu, 2008). Among various image fusion methods,
intensity-hue-saturation (IHS) and Brovey Transforms (BT)
can quickly merge huge amounts of imagery and
simultaneously preserving most of the spatial information
provided that the color distortion is mitigated in the fusion;
as they often face color distortion problems with fused

images (Tu, 2005).
Furthermore, IHS-like and BT-like approaches are indeed
efficient fusion methods since they require only
arithmetical operations without any statistical analysis or
filter design. The Smoothing-filter-based intensity

modulation (SFIM) fusion is one of the most frequently
employed approaches in practice to control the trade of
between the spatial and spectral information. However,
SFIM is not as efficient as BT-like and IHS-like methods in
the fusion of high resolution images. In addition it
preserves more spectral information but suffer more spatial
information loss. Its effectiveness is heavily depends on the
filter design (Tu, 2012). GIHS-BT-SFIM is an approach
which was proposed by Tu et al. as an adjustable pansharpening approach for high resolution satellite imageries.
This method has shown computational advantages among
other fusion methods like wavelet, and could be extended
to different number of bands. The introduced IHS-BTSFIM algorithm incorporates IHS, IHS-BT, BT, BT-SFIM
and SFIM methods by two adjustable parameters. On the
other hand Rahmani et al. proposed an adaptive HIS Pansharpening method, whereby the edges of panchromatic

image combine with the multispectral image to increase the
spatial information as well as preserving the spectral
quality. Another method is to incorporate edge information
on GIHS_BT_SFIM fused image. An improvement is
proposed in this study to combine previous approaches and
test the results. Furthermore, a class-based image fusion
method is introduced in this work to choose the best
parameters for each class.

This contribution has been peer-reviewed. The peer-review was conducted on the basis of the abstract.

139

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W3, 2013
SMPR 2013, 5 – 8 October 2013, Tehran, Iran

 RBT 
R

 Pan  


G
G   BT
 BT 
I  
 BBT 
 B 

2. GIHS-BT-SFIM
2.1. Generalized Intensity-Hue-Saturation (GIHS)
In the HIS color space, intensity (I) is a measure of
brightness, which is sometimes called luminance (L), Hue,
(H) is the color, measured as the angle around a color
wheel or color hexagon (Almoni, 2007), and Saturation is
the amount of color, representing the orthogonal distance
from intensity axis in the HIS color space (Gonzalez,
2002). Generalized IHS (GIHS) or Fast IHS (FIHS) is a
unifying image fusion method in which the low resolution
intensity component (I0) in HIS space is replaced by a graylevel image with higher spatial resolution (Inew or pan) and
transformed back to the original RGB space with the

original H and S components (Tu, 2001).
 Rnew  1 1 / 2

 
Gnew   1 1 / 2
 Bnew  1
2


1 / 2   I new 


1 / 2   v10 

0   v 20 


(1)

R

 
G 
 B 

(3)

And I, is the intensity of resized MS or HS image
calculated as the above.
2.3. Smoothing-Filter-Based
(SFIM)

Intensity

Modulation

SFIM is a BT-like approach, uses a smooth version of pan
image (PL), in lieu of MS image intensity component as
denominator of defined ratio (  BT ). Thereupon, this
method is defined by
 RBT 

R

 Pan  
G
G

 BT 
PL  
 BBT 
 B 

(4)

where, PL is often obtained from a 7*7 mean filter. It is

To develop a computationally efficient method without the
coordinate transformation, (1) can be rewritten as

known that the spatial resolution can be improved by
increasing the mask size of the low-pass filter in the SFIM

method (Tu, 2012).

 Rnew  1 1 / 2

 
Gnew   1 1 / 2

2
 Bnew  1

2.4. An Adjustable GIHS-BT-SFIM

1 / 2   I 0  ( I new  I 0 )   R0   

 

v10
1 / 2  
  G0   


v 20
0 
  B0   


(2)

where   I new  I0 . So, the fused image [ Rnew , Gnew , Bnew ]T
can be easily obtained from the resized original image
[ R0 , G0 , B0 ]T , simply by addition. A merit of the method
lies not only on its fast computing capability for fused
images but also its ability to extend traditional three order
transformations to arbitrary order. That is F i=Mi+  , where
Mi denotes the resized hyperspectral (HS) or multispectral
(MS) image of band i,   Pan  I 0 , and I 0  ((1 / l )



l
i 1


Mi ) ,

l represents the order or the number of bands (Tu, 2001). I0
value can be computed as the band distances, their
weighted (adjusted) average or simply their average same
as the above mentioned, in WorldView-1(Tu, 2012). Using
weighting coefficients on green and blue band (for
IKONOS image) in an attempt to minimize the difference
between I and panchromatic image, leads to spectral
adjusted IHS (SA-IHS) method.
2.2. Brovey Transform (BT)
In contrast to the IHS method, the BT method is a ratio
fusion technique that preserves the relative spectral
contribution of each pixel, but replaces its overall
brightness by the high-resolution panchromatic image (Tu,
2005). It is operated by

As it would be mentioned in the next part, IHS technique
generates significant saturation compression, and the BT
method suffers from saturation stretch. Motivated by this
idea, an adjustable IHS-BT approach was used (Tu, 2005),
then the SFIM method incorporates into the IHS-BT-SFIM
approach (Tu, 2012) by the following equation.
 Rnew 
P


Gnew   I  k .( Pˆ  I )
1
 Bnew 

 R  k2 .( Pˆ  I ) 


G  k2 .( Pˆ  I ) 


 B  k2 .( Pˆ  I ) 

Pˆ  P or PL

(5)

ki is the module selection parameters and defined as to be
0  ki  1 .

3. EDGE ADAPTIVE IHS
An Edge adaptive procedure was introduced by Rahmani et
al. to enforce fidelity away from the edges. They have used
an edge detecting function, h(x) in the IHS fusion method
in the manner of

Fi  M i  h( x)( P  I )

(6)

Where, Fi is the fused image and Mi is the band i of the MS
image. In this case h(x) is equal to one on edges and equal
to zero off edges. Edge adaptive method produces images
with high spectral resolution while maintaining the highquality spatial resolution of the original IHS.

This contribution has been peer-reviewed. The peer-review was conducted on the basis of the abstract.

140

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W3, 2013
SMPR 2013, 5 – 8 October 2013, Tehran, Iran

6. RESULTS

SID (0)
0.0038344
0.0016724

SAM (0)
2.041255
0.8914564

RASE (0)
36.16468
36.237589

RMSE (0)
145.56914
145.86261

QI (1)
0.9427865
0.927844

ERGAS (0)
9.797232
9.7672105

CC MS (1)

)
Grad. (

31.578637

0.2226208
0.2196149

k1  k 2

31.435052

Pˆ  I
 0.5

36.434766

2.29E-07

36.215599

1.64E+00

0.003099

17.119531

2.29E-07

0.0001111

0.0003077

146.65628
145.7741
68.909097

0.8959333
0.9313291

9.7863311

0.9745913

k1  k 2

9.7911045

Pˆ  PL

4.2892944

BTSFIM

0.2215887

0

0.2209034

k1  k 2

0.8339869

BT

Pˆ  I

31.560622

Because of different distortion extent due to each class, it
was interesting to test every method in a class-based
manner. This part was just tested on urban image because
of more distinct nature of the classes and only two different
types of objects defined as urban and vegetation cover. A
class based fusion was tested, which gives different
coefficients for each method due to its class. It means that,
two adjustable parameters, introduced in first part for
defining combination of fusion methods, has changed in a
LUT form and their best values were selected according to
the fusion results assessment in each class. There were 16
miscellaneous cases which were tested and acquainted in
the following part.

1
IHSBT

31.591407

5. CLASS-BASED FUSION APPROACH

k1  k 2



38.038308



) ;   109 and   1010
| P |4 
(9) The best edge detector in this study was Roberts, as
experienced.
h( x)  exp(

IHS

Pˆ  I

CC- p(1)

(8)

Pˆ  P or PL
The edges can be extracted using standard edge detection
methods such as the canny detector (Canny, 1986). There is
another edge detector suggested by Prona and Malik (1990)
whereby the considerable results can be achieved in a
fusion task which was introduced as the best in this study
among other edge detectors in (Rahmani, et al. 2008),

0.9456128

I  k 2.h  x  . Pˆ  I  

 

P
I  k1.h  x  . Pˆ  I

0.949865

I" 

0.9429588

Another approach, which uses panchromatic image on
edges and GIHS-BT-SFIM off edges, can be introduced as
equation (8),

The first study area is an urban region located in Shahriar,
(Longitude: 513 , latitude: 3539 ) Tehran, Iran. The
IKONOS image with four bands (Red, Green, Blue and
Near infrared) and 3 meters spatial resolution in MS and
one panchromatic band with 1 meter spatial resolution was
used in this region (Figure 1). Results (Table 1) are

0.9441899

Pˆ  P or PL

6.1. Urban area

0.4477592

 (7)

Spatial (1)



I  k 2.h  x  . Pˆ  I

0.9793949



0.9800935



0.9718691

P
P  ( I  P).h( x)  k1.h  x  . Pˆ  I

0.9738466

I" 

0.8217719

In many applications such as precision farming and urban
classification, there’s a need to have more precise
information on edges because of the error occurrence is
especially there. Using MS image data off edges and the
minimum saturation on edges presents a good quality on
spatial and spectral information for image based analysis
purposes similar to classification. So, in this improved
method edges are transferred from the GIHS-BT-SFIM
image to the MS image. This approach extracts the edges
from the pan-chromatic image; where there are edges the
IHS-BT-SFIM product was imposed, otherwise, the MS
image was used. In this case the fused multichannel image
can be formed by the new formula,

Two datasets have been tested in this study. First one was a
high resolution image in an urban area and the other a
medium resolution image of an agricultural field. Setting ki
parameters of the GIHS-BT-SFIM, leads us to different
fusion techniques and comparison for each method and
their spectral and spatial quality was simply implemented.
Spatial quality can be judged visually, but subtle color
changes are more difficult to notice in this manner
(Rahmani et al, 2010). In order to compare method results,
spatial and spectral qualities by relying on both visual
inspection and metric performance data were evaluated.
Band-to-band correlation coefficient (CC), distortion
extent, ERGAS, SAM, Q Index (Q-average), RMSE and
RASE are the spectral metrics, and CC and average
gradient are the spatial metrics for analyzing fusion
methods fulfillment (Rahmani, 2008; Alparone, 2008;
Zhang, 2008).

(Reference
values)

4. EDGE ADAPTIVE GIHS-BT-SFIM

1
SFIM

Pˆ  PL

k1  1
k2  0

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141

0.680253

0.831546

40.24464

0.321928

8.846769

0.952452

133.9764

33.28463

1.692252

0.003682

Edge
+
IHSBT
(k=
0.5)

0.680024

0.83469

40.16913

0.320423

8.821502

0.940162

134.2395

33.34998

0.749581

0.001413

Edge
+ BT

0.676058

0.828699

40.2711

0.321717

8.839665

0.913299

134.9936

33.53733

2.30E-07

0.000259

Edge
+ BTSFIM

0.677401

0.830308

40.26946

0.320433

8.841822

0.941824

134.1846

33.33634

1.34E+00

0.002647

Edge
+
SFIM

0.782949

0.42262

37.34387

0.842689

4.142541

0.976479

66.69437

16.56931

2.29E-07

0.000112

0.320391

0.214581

19.92138

0.936497

2.32683

0.99688

0.031071

9.26287

0.086614

0.001128

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W3, 2013
SMPR 2013, 5 – 8 October 2013, Tehran, Iran

Edge
+ IHS

Edge:
HIS
+MS

pan

MS

IHS

IHS+Edge

HIS-BT

HIS-BT+Edge

BT

BT+Edge

BT-SFIM

BT-SFIM+Edge

SFIM

SFIM+Edge

Figure 1:Results for Urban area

SID (0)
0.001672

0.003834

SAM (0)
2.041255
0.891456

RASE (0)
36.16468
36.23759

RMSE (0)
0.121308
0.121552

ERGAS (0)

Grad. (

QI (1)
0.942786

k1  k 2

0.927844

Pˆ  I

9.797232

1
IHSBT

9.767211

0.222621
0.219615

k1  k 2



CC MS (1)

31.57864
31.43505

)

0.945613
0.949865

Pˆ  I

CC- p(1)

Spatial (1)
0.97939495

It is clear that edge adaptive method has improved spectral
information in all procedures. Fusion results are provided
in figure 1.

IHS

0.98009354

Table 1: Assessing the fusion methods in an urban region

The area of interest deals with agricultural fields located
around the village of Biddinghuizen in Flevoland, the
Netherlands, which is characterized by the cultivation of
arable crops (Abkar, 1994). This area represented a typical
agricultural region in the Netherlands. The main crops are
grass, potatoes, cereals, sugar-beets, beans, peas, and
onions. The three spectral bands 3, 4, and 5 of the Landsat
Thematic Mapper (TM) image that was acquired on 7 July
1987 has been used in this study. Results on this region are
in (Table 2 and figure 2).
(Reference
values)

0.000123
1.66E-05
0.000182
0.000104

0.039367
2.35E-07
0.050541
2.35E-07

9.277774
9.348285
9.296099
7.024678

0.031121
0.031357
0.031182
0.023563

0.996142
0.994497
0.995262
0.996388

2.322272
2.330462
2.325192
1.752521

0.93693
0.936093
0.936637
0.964614

19.9217
19.93042
19.9294
18.9778

0.214507
0.214143
0.214311
0.189779

Edge:
SFIM
+MS

0.318512

Edge:
BTSFIM
+MS

0.319598

Edge:
BT
+MS

0.354764

Edge:
HIS_
BT
+MS

0.320256

6.2. Agricultural fields

 0.5

This contribution has been peer-reviewed. The peer-review was conducted on the basis of the abstract.

142

SFIM

Edge
+ IHS

0.435972

0.446119

0.449469

0.469816

0.945269

0.944735

0.950741

0.946362

0.447759

0.94419

0.942959

8.154986

8.15694

8.16501

38.85721

31.68285

31.67301

31.5236

31.67542

38.03831

31.59141

31.56062

0.670549

0.64756

0.644215

0.809155

0.219103

0.21895

0.217567

0.220669

0.833987

0.220903

0.221589

8.3003

10.37553

11.32527

4.576026

9.80137

9.797678

9.779525

9.806361

4.289294

9.791105

9.786331

0.737582

0.740714

0.741658

0.95064

0.908503

0.873873

0.904138

0.918343

0.974591

0.931329

0.895933

0.048911

0.044889

0.04455

0.061239

0.121625

0.122314

0.121714

0.121483

0.057424

0.121478

0.122214

0.33311

0.305718

0.303412

18.25671

36.25917

36.46473

36.28582

36.21683

17.11953

36.2156

36.43477

0.182216

0.438284

0.597513

0.18204

1.769849

0.18204

1.034129

2.136681

2.29E-07

1.63835

2.29E-07

0.00511

0.008474

0.069668

0.000776

0.003627

0.000969

0.002236

0.004311

0.000111

0.003099

0.000308

BT

Pˆ  PL

143

0.401306

0.303222

0.545576

0.442026

6.006096

8.203174

0.899399

0.64545

3.383021

10.2822

0.741403

0.738535

0.019747

0.045262

0.13449

0.308258

0.182216

0.534773

0.00511

0.01595

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W3, 2013
SMPR 2013, 5 – 8 October 2013, Tehran, Iran

Edge:
BTSFIM
+MS

Edge:
SFIM
+MS

MS

IHS+Edge

HIS-BT+Edge

BT+Edge

BT-SFIM+Edge

Table 2: Assessing the fusion methods in an agricultural field

pan

IHS

HIS-BT

BT

BT-SFIM

RMSE (0)c2

SFIM+Edge

CC MS (1)c2

Figure 2: Results for Agricultural field

RMSE (0)c1
CC- p(1)-c2
Class2:
urban

SFIM

CC MS (1)c1

6.3. Class-Based Results (LUT-form)

CC- p(1)-c1

In this part, just the outputs are presented for each group of
modulation parameters. It is worth knowing that class part
results are defined due to some ROIs for each class,
because of the memory limitation for processing the whole
image and it may have some effects on the last results.
Look up table for these parameters are here (Table 3).

(Reference
values)
Class1: veg.

Pˆ  I

0.97186908

k1  k 2

0.97384662

0

0.82177187

BTSFIM

0.96838169

k1  k 2

0.96946102

1

0.95794516

Pˆ  PL

0.96310779

k1  1

0.76962972

k2  0

0.310889

Edge
+
IHSBT
(k=
0.5)

Edge
+ BTSFIM

0.308092

Edge
+ BT

Edge
+
SFIM

Edge:
HIS
+MS

Edge:
HIS_
BT
+MS

Edge:
BT
+MS

This contribution has been peer-reviewed. The peer-review was conducted on the basis of the abstract.

0.299276

9.907305
9.256606
9.590602

8.939193

0.133917
0.154702
0.145262

0.16386

0.963485
0.947669
0.963256

0.903145

4.727121
3.375842
3.952274

2.988708

0.118663
0.154609
0.14442

10.43399

0.13196

10.14125

0.109632

0.957978

0.118897

0.947262

0.821616

9.858437

0.957006

7.579864

K1=0.4
K2=0.2

0.920791

0.129779

6.298416

0.084486

K1=0.8
K2=0.8

0.956358

9.586439

0.962885

0.095161

K1=0.2
K2=0.8

9.305324

0.142437

5.10187

0.927364

K1=0.8
K2=0.6

0.156431

0.961073

0.109794

K1=0.2
K2=0.6

0.947313

4.065888

0.945551

K1=0.8
K2=0.4

3.351619

0.129064

K1=0.2
K2=0.4

0.146586

0.938727

K1=0.8
K2=0.2

0.892511

K1=0.2
K2=0.2

0.905118

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W3, 2013
SMPR 2013, 5 – 8 October 2013, Tehran, Iran

Table 3: Class-based and modulation parameter effects on fusion results. c1;
is for class one (vegetation cover) and c2; is for class2 (urban region).

9.586911
9.880744
8.991131
9.282394

10.18576

0.143304
0.131701
0.167822
0.155797

0.121715

0.962375
0.963491
0.907764
0.94813

0.957035

4.008613
4.863096
2.99262
3.365754

5.828853

0.130391
0.11549
0.103565

0.953542

0.155555

K1=0.6
K2=0.6

0.144887

K1=0.6
K2=0.4

0.940992

K1=0.4
K2=0.8

0.793783

K1=0.4
K2=0.6

0.91122

K1=0.4
K2=0.4

0.947905

7. CONCLUSION
In contrast with previous studies (Rahmani, 2010 & Tu,
2012), which only contemplate the spectral metrics in their
work; there’s a need to have both spatial and spectral
metrics because of the fusion goal; in this study, different
spatial and spectral metrics have been presented and
considering all is obvious that adding edge information
form panchromatic image into GIHS_BT_SFIM product
has not much improvement in various processes (Eq.8).
Using edge information on edges and MS data off edges
has shown spectral enhanced fused images according to
metrics in various methods (Eq.7). Nonetheless, there is
some spatial information loss. So, it is preferable to choose
the best method of fusion due to task aims and image data.
BT method in both images offers the most balanced results
among all according to both spectral and spatial
information, and SFIM has the most spectral information.
Moreover, some of edge detectors’ performance was tested,
and results show the high dependence of edge adaptive
method on the edge detector used.
It is clear that, method sort (the modulation parameter) is
important in image fusion process and the fusion results are
affected by the class type. So, choosing the best modulation
parameters (methods) in a fusion task should be considered.
It is recommended to study on class based results more, in
the future.

9.588235

0.144235

0.963114

3.973864

0.131253

K1=0.6
K2=0.8

0.953234

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SMPR 2013, 5 – 8 October 2013, Tehran, Iran

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